Data Scientist

We're looking for a Data Scientist to own end-to-end ML models and experimentation across the company. This is a high-ownership, execution-heavy role focused on improving conversions, call success rates, and operational efficiency. You'll own problems end-to-end - from metrics and experiments to production models and product impact.

Responsibilities:

• Own end-to-end ML systems for an AI-driven outbound call centre.
• Work on problems directly tied to loan sales, collections, and insurance conversions.
• Drive experiments → product changes → measurable revenue impact.
• Build user / lead scoring models to decide who to call, when, and how.
• Improve conversion rates across outbound funnels (dial → connect → conversation → conversion).
• Work with rich, messy data: call transcripts, recordings, user metadata, and call-level signals.
• Extract insights from within-call behaviour (drop-offs, objections, engagement patterns).
• Design and run A/B experiments on model + product changes.
• Improve LLM agent performance (response quality, handling objections, conversation flow).
• Identify failure points (bad targeting, poor timing, weak conversations) and solve them using data + ML.
• Ship models into production and iterate based on real-world performance.

Requirements:

• Strong Python + SQL (used daily).
• Hands-on ML experience (practical projects/internships).
• Ability to go from raw data → insight → model → deployment.
• Comfort working with noisy, real-world datasets (text, behavioural data).
• Understanding of experimentation / A-B testing.
• Strong problem-solving + ownership mindset.

What Success Looks Like:

• You improve business metrics (conversion, revenue), not just model accuracy.
• Your work directly influences who gets called and how conversations are handled.
• You can break down a funnel and explain where and why it's failing.
• You independently pick problems and drive them end-to-end.

Tech Stack:

• Python, ML libraries (sklearn / PyTorch)
• ClickHouse
• LLMs (Claude, internal systems)
• Experimentation-driven environment